CAREER: Network-Based Signaling Pathway Analysis: Methods and Tools for Turning Theory into Practice

细胞在其环境中接收并响应信号，而这些信号在疾病中经常被破坏。实验可以帮助理解蛋白质如何相互作用来改变细胞的行为；然而，决定以公正的方式测试哪些蛋白质是具有挑战性的。网络或图通常用于表示蛋白质之间的相互作用，其中蛋白质（节点）通过成对相互作用（边）连接在一起。虽然基于网络的方法已经流行了很多年，但这些方法的预测往往很难解释，生物学家也不容易获得这些工具，这极大地减缓了科学发现的潜在步伐。这项研究的目标是开发新的方法，更密切地反映实验生物学家提出的生物学问题，并使这些工具被科学界采用。这项工作将在一个主要的本科机构（PUI）完成，希望从事生物学职业的学生需要计算训练。该项目将通过支持学生和教师发展的地方和国家倡议，在PUIs建立一个计算生物学本科培训计划。该项目将提供以下框架：(a)通过参加会议向本科生介绍计算生物学；(b)在资源有限的情况下为本科生生物学项目实施计算生物学活动和课程。这个项目的结果可以在http://www.reed.edu/biology/ritz/research.html.Cells上找到，它们通过一系列蛋白质-蛋白质相互作用对环境做出反应，这些相互作用统称为信号通路，将细胞外信号传递给靶基因的调节。将信号通路描述为图形的计算方法已经成为理解细胞信号反应中蛋白质之间关系的关键假设生成工具。该项目确定了图论中的一个统一概念——计算图中的定向、连接路径——并将这一想法应用于生物学多个领域提出的信号通路分析问题。将开发新的寻路算法，以疾病中失调的信号作为案例研究，产生主动信号的机制假设。这些寻径算法将应用于细胞和发育生物学中的信号通路分析，包括调节细胞形状（形态发生）和眼睛发育（视网膜神经发生）变化的途径。与生物学家的密切合作将有助于为开发易于使用的工具提供信息，并扩大其在其他领域的适用性。最终目标将建立超图，有向图的推广，作为信号的改进数学表示。该项目产生的新方法的集合，以及这些工具作为生物学其他领域假设生成引擎的示范，将是加速假设生成-验证-测试研究周期的重要一步。这些方法的采用者的生物学贡献将证明这项工作对科学知识和发现的影响超出了本项目的计算贡献。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Cells receive and respond to signals in their environment, and these signals are often disrupted in disease. Experiments can help understand how proteins interact with each other to alter the cell's behavior; however deciding which proteins to test in an unbiased manner is challenging. Networks, or graphs, are commonly used to represent interactions among proteins, where proteins (nodes) are linked by pairwise interactions (edges). While network-based methods have been popular for many years, predictions from these methods are often challenging to interpret and the tools have not been made easily accessible to biologists, dramatically slowing the potential pace of scientific discovery. The goal of this research is to develop novel methods that more closely reflect the biological questions posed by experimental biologists, and enable the adoption of such tools by the scientific community. This work will be accomplished at a primarily undergraduate institution (PUI), and students who wish to pursue careers in biology need computational training. The project will establish a program for undergraduate training in computational biology at PUIs through local and national initiatives that support both student and faculty development. This project will offer frameworks for (a) introducing computational biology to undergraduates through conference attendance and (b) implementing computational biology activities and courses for undergraduate biology programs with limited resources. Results from this project can be found at http://www.reed.edu/biology/ritz/research.html.Cells respond to their environment using a series of protein-protein interactions, collectively referred to as signaling pathways, that transfer extracellular signals to the regulation of target genes. Computational methods that describe signaling pathways as graphs have been critical hypothesis-generation tools for understanding the relationship among proteins in cellular signaling response. This project identifies a unifying concept in graph theory -- that of computing directed, connected paths in graphs -- and applies this idea to signaling pathway analysis questions posed in multiple fields of biology. Novel path-finding algorithms will be developed to generate mechanistic hypotheses of active signaling, using dysregulated signaling in disease as a case study. These path-finding algorithms will be applied to signaling pathway analysis in cellular and developmental biology, including pathways that regulate changes in cell shape (morphogenesis) and eye development (retinal neurogenesis). Close collaborations with biologists will help inform the development of easy-to-use tools and broaden their applicability to other fields. The final aim will establish hypergraphs, a generalization of directed graphs, as an improved mathematical representation of signaling. The collection of novel methods produced by this project, along with a demonstration that these tools serve as hypothesis generation engines for other fields in biology, will be a significant step towards accelerating the hypothesis generation-validation-testing research cycle. Biological contributions by adopters of these methods will exponentiate this work's impact on scientific knowledge and discovery beyond the computational contributions in this project.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.